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Water bodies are ubiquitous features in Arctic wetlands. Ponds, i.e., waters with a surface area smaller than 104 m2, have been recognized as hotspots of biological activity and greenhouse gas emissions but are not well inventoried. This study aimed to identify common characteristics

Water bodies are ubiquitous features in Arctic wetlands. Ponds, i.e., waters with a surface area smaller than 104 m2, have been recognized as hotspots of biological activity and greenhouse gas emissions but are not well inventoried. This study aimed to identify common characteristics of three Arctic wetlands including water body size and abundance for different spatial resolutions, and the potential of Landsat-5 TM satellite data to show the subpixel fraction of water cover (SWC) via the surface albedo. Water bodies were mapped using optical and radar satellite data with resolutions of 4mor better, Landsat-5 TM at 30mand the MODIS water mask (MOD44W) at 250m resolution. Study sites showed similar properties regarding water body distributions and scaling issues. Abundance-size distributions showed a curved pattern on a log-log scale with a flattened lower tail and an upper tail that appeared Paretian. Ponds represented 95% of the total water body number. Total number of water bodies decreased with coarser spatial resolutions. However, clusters of small water bodies were merged into single larger water bodies leading to local overestimation of water surface area. To assess the uncertainty of coarse-scale products, both surface water fraction and the water body size distribution should therefore be considered. Using Landsat surface albedo to estimate SWC across different terrain types including polygonal terrain and drained thermokarst basins proved to be a robust approach. However, the albedo–SWC relationship is site specific and needs to be tested in other Arctic regions. These findings present a baseline to better represent small water bodies of Arctic wet tundra environments in regional as well as global ecosystem and climate models.
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The quantification of forest above-ground biomass (AGB) is important for such broader applications as decision making, forest management, carbon (C) stock change assessment and scientific applications, such as C cycle modeling. However, there is a great uncertainty related to the estimation of forest

The quantification of forest above-ground biomass (AGB) is important for such broader applications as decision making, forest management, carbon (C) stock change assessment and scientific applications, such as C cycle modeling. However, there is a great uncertainty related to the estimation of forest AGB, especially in the tropics. The main goal of this study was to test a combination of field data and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest AGB in the Miombo savanna woodlands of Mozambique (East Africa). A machine learning algorithm, based on bagging stochastic gradient boosting (BagSGB), was used to model forest AGB as a function of ALOS PALSAR Fine Beam Dual (FBD) backscatter intensity metrics. The application of this method resulted in a coefficient of correlation (R) between observed and predicted (10-fold cross-validation) forest AGB values of 0.95 and a root mean square error of 5.03 Mg·ha−1. However, as a consequence of using bootstrap samples in combination with a cross validation procedure, some bias may have been introduced, and the reported cross validation statistics could be overoptimistic. Therefore and as a consequence of the BagSGB model, a measure of prediction variability (coefficient of variation) on a pixel-by-pixel basis was also produced, with values ranging from 10 to 119% (mean = 25%) across the study area. It provides additional and complementary information regarding the spatial distribution of the error resulting from the application of the fitted model to new observations.
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The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past

The long term archiving of both Landsat imagery and wheat yield mapping datasets sensed by precision agriculture technology has the potential through the development of statistical relationships to predict high resolution estimates of wheat yield over large areas for multiple seasons. Quantifying past yield performance over different growing seasons can inform agricultural management decisions ranging from fertilizer applications at the sub-paddock scale to changes in land use at a landscape scale. However, an understanding of the magnitude of prediction errors is needed. In this study, we examine the predictive wheat yield relationships developed from Normalised Difference Vegetation Index (NDVI) acquired Landsat imagery and combine-mounted yield monitors for three Western Australian farms over different growing seasons. We further analysed their predictive capability when these relationships are used to extrapolate yield from one farm to another. Over all seasons, the best predictions were achieved with imagery acquired in September. Of the five seasons reviewed, three showed very reasonable prediction accuracies, with the low and high rainfall years providing good predictions. Medium rainfall years showed the greatest variation in prediction accuracy with marginal to poor predictions resulting from narrow ranges of measured wheat yield and NDVI values. These results demonstrate the potential benefit of fusing together two high resolution datasets to create robust wheat yield prediction models over different growing seasons, the outputs of which can be used to inform agricultural decision making.
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The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements

The performance of a new algorithm for binary snow cover monitoring based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images at 250 m resolution is validated using snow cover maps (SCA) based on Landsat 7 ETM+ images and in situ snow depth measurements from ground stations in selected test sites in Central Europe. The advantages of the proposed algorithm are the improved ground resolution of 250 m and the near real-time availability with respect to the 500 m standard National Aeronautics and Space Administration (NASA) MODIS snow products (MOD10 and MYD10). It allows a more accurate snow cover monitoring at a local scale, especially in mountainous areas characterized by large landscape heterogeneity. The near real-time delivery makes the product valuable as input for hydrological models, e.g., for flood forecast. A comparison to sixteen snow cover maps derived from Landsat ETM/ETM+ showed an overall accuracy of 88.1%, which increases to 93.6% in areas outside of forests. A comparison of the SCA derived from the proposed algorithm with standard MODIS products, MYD10 and MOD10, indicates an agreement of around 85.4% with major discrepancies in forested areas. The validation of MODIS snow cover maps with 148 in situ snow depth measurements shows an accuracy ranging from 94% to around 82%, where the lowest accuracies is found in very rugged terrain restricted to in situ stations along north facing slopes, which lie in shadow in winter during the early morning acquisition.
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Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper

Crop coefficient (Kc)-based estimation of crop evapotranspiration is one of the most commonly used methods for irrigation water management. However, uncertainties of the generalized dual crop coefficient (Kc) method of the Food and Agricultural Organization of the United Nations Irrigation and Drainage Paper No. 56 can contribute to crop evapotranspiration estimates that are substantially different from actual crop evapotranspiration. Similarities between the crop coefficient curve and a satellite-derived vegetation index showed potential for modeling a crop coefficient as a function of the vegetation index. Therefore, the possibility of directly estimating the crop coefficient from satellite reflectance of a crop was investigated. The Kc data used in developing the relationship with NDVI were derived from back-calculations of the FAO-56 dual crop coefficients procedure using field data obtained during 2007 from representative US cropping systems in the High Plains from AmeriFlux sites. A simple linear regression model ( ) is developed to establish a general relationship between a normalized difference vegetation index (NDVI) from a moderate resolution satellite data (MODIS) and the crop coefficient (Kc) calculated from the flux data measured for different crops and cropping practices using AmeriFlux towers. There was a strong linear correlation between the NDVI-estimated Kc and the measured Kc with an r2 of 0.91 and 0.90, while the root-mean-square error (RMSE) for Kc in 2006 and 2007 were 0.16 and 0.19, respectively. The procedure for quantifying crop coefficients from NDVI data presented in this paper should be useful in other regions of the globe to understand regional irrigation water consumption.
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The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications.

The natural environment and livelihoods in the Lower Mekong Basin (LMB) are significantly affected by the annual hydrological cycle. Monitoring of soil moisture as a key variable in the hydrological cycle is of great interest in a number of Hydrological and agricultural applications. In this study we evaluated the quality and spatiotemporal variability of the soil moisture product retrieved from C-band scatterometers data across the LMB sub-catchments. The soil moisture retrieval algorithm showed reasonable performance in most areas of the LMB with the exception of a few sub-catchments in the eastern parts of Laos, where the land cover is characterized by dense vegetation. The best performance of the retrieval algorithm was obtained in agricultural regions. Comparison of the available in situ evaporation data in the LMB and the Basin Water Index (BWI), an indicator of the basin soil moisture condition, showed significant negative correlations up to R = −0.85. The inter-annual variation of the calculated BWI was also found corresponding to the reported extreme hydro-meteorological events in the Mekong region. The retrieved soil moisture data show high correlation (up to R = 0.92) with monthly anomalies of precipitation in non-irrigated regions. In general, the seasonal variability of soil moisture in the LMB was well captured by the retrieval method. The results of analysis also showed significant correlation between El Niño events and the monthly BWI anomaly measurements particularly for the month May with the maximum correlation of R = 0.88.
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Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.
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The purpose of this paper is to estimate the water balance in a semi-arid environment with limited in situ data using a remote sensing approach. We focus on the Lake Manyara catchment, located within the East African Rift of northern Tanzania. We use

The purpose of this paper is to estimate the water balance in a semi-arid environment with limited in situ data using a remote sensing approach. We focus on the Lake Manyara catchment, located within the East African Rift of northern Tanzania. We use a distributed conceptual hydrological model driven by remote sensing data to study the spatial and temporal variability of water balance parameters within the catchment. Satellite gravimetry GRACE data is used to verify the trends of the inferred lake level changes. The results show that the lake undergoes high spatial and temporal variations, characteristic of a semi-arid climate with high evaporation and low rainfall. We observe that the Lake Manyara water balance and GRACE equivalent water depth show comparable trends; a decrease after 2002 followed by a sharp increase in 2006–2007. Our modeling confirms the importance of the 2006–2007 Indian Ocean Dipole fluctuation in replenishing the groundwater reservoirs of East Africa. We thus demonstrate that water balance modeling can be performed successfully using remote sensing data even in complex climatic settings. Despite the small size of Lake Manyara, GRACE data showed great potential for hydrological research on smaller un-gauged lakes and catchments in similar semi-arid environments worldwide. The water balance information can be used for further analysis of lake variations in relation to soil erosion, climate and land cover/land use change as well as different lake management and conservation scenarios.
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High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs

High resolution Digital Surface Models (DSMs) produced from airborne laser-scanning or stereo satellite images provide a very useful source of information for automated 3D building reconstruction. In this paper an investigation is reported about extraction of 3D building models from high resolution DSMs and orthorectified images produced from Worldview-2 stereo satellite imagery. The focus is on the generation of 3D models of parametric building roofs, which is the basis for creating Level Of Detail 2 (LOD2) according to the CityGML standard. In particular the building blocks containing several connected buildings with tilted roofs are investigated and the potentials and limitations of the modeling approach are discussed. The edge information extracted from orthorectified image has been employed as additional source of information in 3D reconstruction algorithm. A model driven approach based on the analysis of the 3D points of DSMs in a 2D projection plane is proposed. Accordingly, a building block is divided into smaller parts according to the direction and number of existing ridge lines for parametric building reconstruction. The 3D model is derived for each building part, and finally, a complete parametric model is formed by merging the 3D models of the individual building parts and adjusting the nodes after the merging step. For the remaining building parts that do not contain ridge lines, a prismatic model using polygon approximation of the corresponding boundary pixels is derived and merged to the parametric models to shape the final model of the building. A qualitative and quantitative assessment of the proposed method for the automatic reconstruction of buildings with parametric roofs is then provided by comparing the final model with the existing surface model as well as some field measurements.
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Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal

Real-time estimation of crop progress stages is critical to the US agricultural economy and decision making. In this paper, a Hidden Markov Model (HMM) based method combining multisource features has been presented. The multisource features include mean Normalized Difference Vegetation Index (NDVI), fractal dimension, and Accumulated Growing Degree Days (AGDDs). In our case, these features are global variable, and measured in the state-level. Moreover, global feature in each Day of Year (DOY) would be impacted by multiple progress stages. Therefore, a mixture model is employed to model the observation probability distribution with all possible stage components. Then, a filtering based algorithm is utilized to estimate the proportion of each progress stage in the real-time. Experiments are conducted in the states of Iowa, Illinois and Nebraska in the USA, and our results are assessed and validated by the Crop Progress Reports (CPRs) of the National Agricultural Statistics Service (NASS). Finally, a quantitative comparison and analysis between our method and spectral pixel-wise based methods is presented. The results demonstrate the feasibility of the proposed method for the estimation of corn progress stages. The proposed method could be used as a supplementary tool in aid of field survey. Moreover, it also can be used to establish the progress stage estimation model for different types of crops.
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We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant

We introduce and test the performance of two sampling methods that utilize distance distributions of laser point clouds in terrestrial and mobile laser scanning geometries. The methods are leveled histogram sampling and inversely weighted distance sampling. The methods aim to reduce a significant portion of the laser point cloud data while retaining most characteristics of the full point cloud. We test the methods in three case studies in which data were collected using a different terrestrial or mobile laser scanning system in each. Two reference methods, uniform sampling and linear point picking, were used for result comparison. The results demonstrate that correctly selected distance-sensitive sampling techniques allow higher point removal than the references in all the tested case studies.
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Shanghai is a modern metropolis characterized by high urban density and anthropogenic ground motions. Although traditional deformation monitoring methods, such as GPS and spirit leveling, are reliable to millimeter accuracy, the sparse point subsidence information makes understanding large areas difficult. Multiple temporal space-borne

Shanghai is a modern metropolis characterized by high urban density and anthropogenic ground motions. Although traditional deformation monitoring methods, such as GPS and spirit leveling, are reliable to millimeter accuracy, the sparse point subsidence information makes understanding large areas difficult. Multiple temporal space-borne synthetic aperture radar interferometry is a powerful high-accuracy (sub-millimeter) remote sensing tool for monitoring slow ground deformation for a large area with a high point density. In this paper, the Interferometric Point Target Time Series Analysis method is used to extract ground subsidence rates in Shanghai based on 31 C-Band and 35 X-Band synthetic aperture radar (SAR) images obtained by Envisat and COSMO SkyMed (CSK) satellites from 2007 to 2010. A significant subsidence funnel that was detected is located in the junction place between the Yangpu and the Hongkou Districts. A t-test is formulated to judge the agreements between the subsidence results obtained by SAR and by spirit leveling. In addition, four profile lines crossing the subsidence funnel area are chosen for a comparison of ground subsidence rates, which were obtained by the two different band SAR images, and show a good agreement.
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Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual

Tree parameter determinations using airborne Light Detection and Ranging (LiDAR) have been conducted in many forest types, including coniferous, boreal, and deciduous. However, there are only a few scientific articles discussing the application of LiDAR to mangrove biophysical parameter extraction at an individual tree level. The main objective of this study was to investigate the potential of using LiDAR data to estimate the biophysical parameters of mangrove trees at an individual tree scale. The Variable Window Filtering (VWF) and Inverse Watershed Segmentation (IWS) methods were investigated by comparing their performance in individual tree detection and in deriving tree position, crown diameter, and tree height using the LiDAR-derived Canopy Height Model (CHM). The results demonstrated that each method performed well in mangrove forests with a low percentage of crown overlap conditions. The VWF method yielded a slightly higher accuracy for mangrove parameter extractions from LiDAR data compared with the IWS method. This is because the VWF method uses an adaptive circular filtering window size based on an allometric relationship. As a result of the VWF method, the position measurements of individual tree indicated a mean distance error value of 1.10 m. The individual tree detection showed a kappa coefficient of agreement (K) value of 0.78. The estimation of crown diameter produced a coefficient of determination (R2) value of 0.75, a Root Mean Square Error of the Estimate (RMSE) value of 1.65 m, and a Relative Error (RE) value of 19.7%. Tree height determination from LiDAR yielded an R2 value of 0.80, an RMSE value of 1.42 m, and an RE value of 19.2%. However, there are some limitations in the mangrove parameters derived from LiDAR. The results indicated that an increase in the percentage of crown overlap (COL) results in an accuracy decrease of the mangrove parameters extracted from the LiDAR-derived CHM, particularly for crown measurements. In this study, the accuracy of LiDAR-derived biophysical parameters in mangrove forests using the VWF and IWS methods is lower than in coniferous, boreal, pine, and deciduous forests. An adaptive allometric equation that is specific for the level of tree density and percentage of crown overlap is a solution for improving the predictive accuracy of the VWF method.
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This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes,

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos.
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Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability

Insular Southeast Asia is a hotspot of humid tropical forest cover loss. A sample-based monitoring approach quantifying forest cover loss from Landsat imagery was implemented to estimate gross forest cover loss for two eras, 1990–2000 and 2000–2005. For each time interval, a probability sample of 18.5 km × 18.5 km blocks was selected, and pairs of Landsat images acquired per sample block were interpreted to quantify forest cover area and gross forest cover loss. Stratified random sampling was implemented for 2000–2005 with MODIS-derived forest cover loss used to define the strata. A probability proportional to x (πpx) design was implemented for 1990–2000 with AVHRR-derived forest cover loss used as the x variable to increase the likelihood of including forest loss area in the sample. The estimated annual gross forest cover loss for Malaysia was 0.43 Mha/yr (SE = 0.04) during 1990–2000 and 0.64 Mha/yr (SE = 0.055) during 2000–2005. Our use of the πpx sampling design represents a first practical trial of this design for sampling satellite imagery. Although the design performed adequately in this study, a thorough comparative investigation of the πpx design relative to other sampling strategies is needed before general design recommendations can be put forth.
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The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical–conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing.
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Image acquisition systems based on multi-head arrangement of digital cameras are attractive alternatives enabling a larger imaging area when compared to a single frame camera. The calibration of this kind of system can be performed in several steps or by using simultaneous bundle

Image acquisition systems based on multi-head arrangement of digital cameras are attractive alternatives enabling a larger imaging area when compared to a single frame camera. The calibration of this kind of system can be performed in several steps or by using simultaneous bundle adjustment with relative orientation stability constraints. The paper will address the details of the steps of the proposed approach for system calibration, image rectification, registration and fusion. Experiments with terrestrial and aerial images acquired with two Fuji FinePix S3Pro cameras were performed. The experiments focused on the assessment of the results of self-calibrating bundle adjustment with and without relative orientation constraints and the effects to the registration and fusion when generating virtual images. The experiments have shown that the images can be accurately rectified and registered with the proposed approach, achieving residuals smaller than one pixel.
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Temperature and precipitation have been separately reported to be the main factors affecting the Normalized Difference Vegetation Index (NDVI) in the Tibetan Plateau. The effects of the main climatic factors on the yearly maximum NDVI (MNDVI) in the Tibetan Plateau were examined on

Temperature and precipitation have been separately reported to be the main factors affecting the Normalized Difference Vegetation Index (NDVI) in the Tibetan Plateau. The effects of the main climatic factors on the yearly maximum NDVI (MNDVI) in the Tibetan Plateau were examined on different scales. The result underscored the observation that both precipitation and temperature affect MNDVI based on weather stations or physico-geographical regions. Precipitation is the main climatic factor that affects the vegetation cover in the entire Tibetan Plateau. Both annual mean precipitation and annual mean precipitation of the growing period are related with MNDVI, and the positive correlations are manifested in a linear manner. By comparison, the weakly correlated current between MNDVI and all the temperature indexes is observed in the study area.
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Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management

Bark beetles cause widespread damages in the coniferous-dominated forests of central Europe and North America. In the future, areas affected by bark beetles may further increase due to climate change. However, the early detection of the bark beetle green attack can guide management decisions to prevent larger damages. For this reason, a field-based bark beetle monitoring program is currently implemented in Germany. The combination of remote sensing and field data may help minimizing the reaction time and reducing costs of monitoring programs covering large forested areas. In this case study, RapidEye and TerraSAR-X data were analyzed separately and in combination to detect bark beetle green attack. The remote sensing data were acquired in May 2009 for a study site in south-west Germany. In order to distinguish healthy areas and areas affected by bark beetle green attack, three statistical approaches were compared: generalized linear models (GLM), maximum entropy (ME) and random forest (RF). The spatial scale (minimum mapping unit) was 78.5 m2. TerraSAR-X data resulted in fair classification accuracy with a cross-validated Cohen’s Kappa Coefficient (kappa) of 0.23. RapidEye data resulted in moderate classification accuracy with a kappa of 0.51. The highest classification accuracy was obtained by combining the TerraSAR-X and RapidEye data, resulting in a kappa of 0.74. The accuracy of ME models was considerably higher than the accuracy of GLM and RF models.
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Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized

Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.
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This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation

This work discusses the accuracies of geophysical model functions (GMFs) for retrieval of sea surface wind speed from satellite-borne Synthetic Aperture Radar (SAR) images in Japanese coastal waters characterized by short fetches and variable atmospheric stability conditions. In situ observations from two validation sites, Hiratsuka and Shirahama, are used for comparison of the retrieved sea surface wind speeds using CMOD (C-band model)4, CMOD_IFR2, CMOD5 and CMOD5.N. Of all the geophysical model functions (GMFs), the latest C-band GMF, CMOD5.N, has the smallest bias and root mean square error at both sites. All of the GMFs exhibit a negative bias in the retrieved wind speed. In order to understand the reason for this bias, all SAR-retrieved wind speeds are separated into two categories: onshore wind (blowing from sea to land) and offshore wind (blowing from land to sea). Only offshore winds were found to exhibit the large negative bias, and short fetches from the coastline may be a possible reason for this. Moreover, it is clarified that in both the unstable and stable conditions, CMOD5.N has atmospheric stability effectiveness, and can keep the same accuracy with CMOD5 in the neutral condition. In short, at the moment, CMOD5.N is thought to be the most promising GMF for the SAR wind speed retrieval with the atmospheric stability correction in Japanese coastal waters, although there is ample room for future improvement for the effect from short fetch.
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This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements

This paper considers an experimental approach for assessing algorithms used to exploit remotely sensed data. The approach employs synthetic images that are generated using physical models to make them more realistic while still providing ground truth data for quantitative evaluation. This approach complements the common approach of using real data and/or simple model-generated data. To demonstrate the value of such an approach, the behavior of the FastICA algorithm as a hyperspectral unmixing technique is evaluated using such data. This exploration leads to a number of useful insights such as: (1) the need to retain more dimensions than indicated by eigenvalue analysis to obtain near-optimal results; (2) conditions in which orthogonalization of unmixing vectors is detrimental to the exploitation results; and (3) a means for improving FastICA unmixing results by recognizing and compensating for materials that have been split into multiple abundance maps.
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This study aims to investigate the impact of the spatial size of the study domain on the performance of the triangle method using progressively smaller domains and Moderate Resolution Imaging Spectroradiometer (MODIS) observations in the Heihe River basin located in the arid region

This study aims to investigate the impact of the spatial size of the study domain on the performance of the triangle method using progressively smaller domains and Moderate Resolution Imaging Spectroradiometer (MODIS) observations in the Heihe River basin located in the arid region of northwestern China. Data from 10 clear-sky days during the growing season from April to September 2009 were used. Results show that different dry/wet edges in the surface temperature-vegetation index space directly led to the deviation of evapotranspiration (ET) estimates due to the variation of the spatial domain size. The slope and the intercept of the limiting edges are dependent on the range and the maximum of surface temperature over the spatial domain. The difference of the limiting edges between different domain sizes has little impact on the spatial pattern of ET estimates, with the Pearson correlation coefficient ranging from 0.94 to 1.0 for the 10 pairs of ET estimates at different domain scales. However, it has a larger impact on the degree of discrepancies in ET estimates between different domain sizes, with the maximum of 66 W∙m−2. The largest deviation of ET estimates between different domain sizes was found at the beginning of the growing season.
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Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or “canopy layering,” is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at

Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or “canopy layering,” is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions—one categorical and one continuous—are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA’s Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities.
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Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally

Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the availability of long time series as needed by the majority of the yield prediction methods used today.
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